# Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation

arXiv: https://arxiv.org/abs/2204.11618 · HTML: https://mlsanity.com/paper/2204.11618/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 22 Apr 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Fully Automatic Wound Segmentation with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2010.05855/) (2020)
- [FUSeg: The Foot Ulcer Segmentation Challenge](https://mlsanity.com/paper/2201.00414/) (2022)
- [Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation](https://mlsanity.com/paper/2010.03341/) (2020)
- [Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation](https://mlsanity.com/paper/1708.01928/) (2017)
- [Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers](https://mlsanity.com/paper/2104.03068/) (2021)
- [Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks](https://mlsanity.com/paper/2109.01408/) (2021)
- [Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary](https://mlsanity.com/paper/2111.10376/) (2021)
- [A Cloud-based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers](https://mlsanity.com/paper/2105.07763/) (2021)
- [Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks](https://mlsanity.com/paper/1711.10449/) (2017)
- [Development of Diabetic Foot Ulcer Datasets: An Overview](https://mlsanity.com/paper/2201.00163/) (2022)

## Built on by (most cited first, 19 of 19)

- [Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models](https://mlsanity.com/paper/2308.07706/) (2023)
- [FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation](https://mlsanity.com/paper/2305.02961/) (2023)
- [On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness](https://mlsanity.com/paper/2209.06078/) (2022)
- [VLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with Lightweight Blocks](https://mlsanity.com/paper/2405.06196/) (2024)
- [HarDNet-DFUS: An Enhanced Harmonically-Connected Network for Diabetic Foot Ulcer Image Segmentation and Colonoscopy Polyp Segmentation](https://mlsanity.com/paper/2209.07313/) (2022)
- [Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis](https://mlsanity.com/paper/2311.15836/) (2023)
- [Wound Tissue Segmentation in Diabetic Foot Ulcer Images Using Deep Learning: A Pilot Study](https://mlsanity.com/paper/2406.16012/) (2024)
- [TuneVLSeg: Prompt Tuning Benchmark for Vision-Language Segmentation Models](https://mlsanity.com/paper/2410.05239/) (2024)
- [Wound3DAssist: A Practical Framework for 3D Wound Assessment](https://mlsanity.com/paper/2508.17635/) (2025)
- [An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Merging](https://mlsanity.com/paper/2410.03359/) (2024)
- [Deep Learning for Wound Tissue Segmentation: A Comprehensive Evaluation using A Novel Dataset](https://mlsanity.com/paper/2502.10652/) (2025)
- [WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care](https://mlsanity.com/paper/2504.06185/) (2025)
- [Quantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/2304.12987/) (2023)
- [Early Explorations of Lightweight Models for Wound Segmentation on Mobile Devices](https://mlsanity.com/paper/2407.07605/) (2024)
- [Beyond Labels: Zero-Shot Diabetic Foot Ulcer Wound Segmentation with Self-attention Diffusion Models and the Potential for Text-Guided Customization](https://mlsanity.com/paper/2504.17628/) (2025)
- [MedPEFT-CL: Dual-Phase Parameter-Efficient Continual Learning with Medical Semantic Adapter and Bidirectional Memory Consolidation](https://mlsanity.com/paper/2511.17668/) (2025)
- [Diabetic Foot Ulcer Grand Challenge 2022 Summary](https://mlsanity.com/paper/2304.12001/) (2023)
- [ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models](https://mlsanity.com/paper/2305.18993/) (2023)
- [Gaussian Random Fields as an Abstract Representation of Patient Metadata for Multimodal Medical Image Segmentation](https://mlsanity.com/paper/2503.05214/) (2025)

## Abstract (arXiv)

Diabetic foot ulcer is a severe condition that requires close monitoring and management. For training machine learning methods to auto-delineate the ulcer, clinical staff must provide ground truth annotations. In this paper, we propose a new diabetic foot ulcers dataset, namely DFUC2022, the largest segmentation dataset where ulcer regions were manually delineated by clinicians. We assess whether the clinical delineations are machine interpretable by deep learning networks or if image processing refined contour should be used. By providing benchmark results using a selection of popular deep learning algorithms, we draw new insights into the limitations of DFU wound delineation and report on the associated issues. This paper provides some observations on baseline models to facilitate DFUC2022 Challenge in conjunction with MICCAI 2022. The leaderboard will be ranked by Dice score, where the best FCN-based method is 0.5708 and DeepLabv3+ achieved the best score of 0.6277. This paper demonstrates that image processing using refined contour as ground truth can provide better agreement with machine predicted results. DFUC2022 will be released on the 27th April 2022.